Loading...
Graph State Machines
Finite state machines implemented as graphs for workflow control
Core Mechanism
Graph State Machines represent control flow as states (nodes) and event-driven transitions (edges) with guard conditions and actions. Statecharts augment this with hierarchical states, orthogonal regions (parallelism), and history to capture complex real-world behavior. This gives deterministic, auditable flow control for agent/tool invocations, approvals, retries, and human-in-the-loop checkpoints.
Workflow / Steps
- Enumerate states (initial, intermediate, terminal) and define state/context schema.
- List events; define transitions with guards and actions (include error/timeouts as events).
- Design the graph: priorities/defaults per state; add hierarchy/parallel regions if using statecharts.
- Implement pure transition logic; run side effects in effect handlers with retries and idempotency.
- Persist state + minimal context; version the machine; enable visualization from source.
- Test transitions (table-driven), fuzz guards, and validate invariants; add observability.
Best Practices
When NOT to Use
Simple linear flows where a straight-line function or DAG is clearer and cheaper.
Heavy dataflow/ETL where stream/DAG engines fit better than event-guard machines.
Ultra low-latency paths sensitive to event loop/guard evaluation overhead.
Strict ACID cross-service transactions; prefer workflow engines with sagas/transactions.
Common Pitfalls
State explosion from encoding fine-grained data conditions as states rather than guards.
Ambiguous or overlapping guards; missing default transitions → nondeterminism.
Embedding side effects inside transitions → non-determinism and retry hazards.
Missing persistence/idempotency → unrecoverable or duplicated effects on retry.
Parallel regions without proper isolation → races and deadlocks.
Key Features
KPIs / Success Metrics
Token / Resource Usage
Drivers: per-state LLM/tool calls, validation/self-check prompts, and context growth in state data.
Controls: per-state token budgets; summarize/trim context; cache deterministic steps; bound concurrency.
Storage/CPU: persist minimal state + references; avoid logging sensitive context; instrument per-transition cost.
Best Use Cases
Order/approval/fulfillment flows with retries, compensations, and human approvals.
Agent/dialog controllers gating tool calls and safety checks per step.
Compliance workflows requiring deterministic replay and audit trails.
Device/process controllers with hierarchical/parallel modes.
References & Further Reading
Academic Papers
Implementation Guides
Tools & Libraries
- XState/Statecharts, AWS Step Functions, Temporal, Camunda BPMN 2.0
- LangGraph, LlamaIndex Workflows
Community & Discussions
Graph State Machines
Finite state machines implemented as graphs for workflow control
Core Mechanism
Graph State Machines represent control flow as states (nodes) and event-driven transitions (edges) with guard conditions and actions. Statecharts augment this with hierarchical states, orthogonal regions (parallelism), and history to capture complex real-world behavior. This gives deterministic, auditable flow control for agent/tool invocations, approvals, retries, and human-in-the-loop checkpoints.
Workflow / Steps
- Enumerate states (initial, intermediate, terminal) and define state/context schema.
- List events; define transitions with guards and actions (include error/timeouts as events).
- Design the graph: priorities/defaults per state; add hierarchy/parallel regions if using statecharts.
- Implement pure transition logic; run side effects in effect handlers with retries and idempotency.
- Persist state + minimal context; version the machine; enable visualization from source.
- Test transitions (table-driven), fuzz guards, and validate invariants; add observability.
Best Practices
When NOT to Use
Simple linear flows where a straight-line function or DAG is clearer and cheaper.
Heavy dataflow/ETL where stream/DAG engines fit better than event-guard machines.
Ultra low-latency paths sensitive to event loop/guard evaluation overhead.
Strict ACID cross-service transactions; prefer workflow engines with sagas/transactions.
Common Pitfalls
State explosion from encoding fine-grained data conditions as states rather than guards.
Ambiguous or overlapping guards; missing default transitions → nondeterminism.
Embedding side effects inside transitions → non-determinism and retry hazards.
Missing persistence/idempotency → unrecoverable or duplicated effects on retry.
Parallel regions without proper isolation → races and deadlocks.
Key Features
KPIs / Success Metrics
Token / Resource Usage
Drivers: per-state LLM/tool calls, validation/self-check prompts, and context growth in state data.
Controls: per-state token budgets; summarize/trim context; cache deterministic steps; bound concurrency.
Storage/CPU: persist minimal state + references; avoid logging sensitive context; instrument per-transition cost.
Best Use Cases
Order/approval/fulfillment flows with retries, compensations, and human approvals.
Agent/dialog controllers gating tool calls and safety checks per step.
Compliance workflows requiring deterministic replay and audit trails.
Device/process controllers with hierarchical/parallel modes.
References & Further Reading
Academic Papers
Implementation Guides
Tools & Libraries
- XState/Statecharts, AWS Step Functions, Temporal, Camunda BPMN 2.0
- LangGraph, LlamaIndex Workflows